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Articles tagged Design of Experiments (DOE)

The arrangement in which an experimental program is to be conducted and selection of the levels of one or more factors or factor combinations to be included in the experiment. It is also known as statistical design of experiment, as it entails a versatile statistical approach to screen for important factors, optimize factor settings, or detect important factor relationships or interactions.

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DOE-7: Analyse Factorial Design with Minitab: Case Study in Maximizing Fatigue Strength

DOE-7: Analyse Factorial Design with Minitab: Case Study in Maximizing Fatigue Strength

Dear friends, this is part-2 of our video on Design of Experiments using Minitab. In part-1, Hemant Urdhwareshe had explained how to create design using Minitab. In this part-2 of the video, Hemant has illustrated complete details of analysis of the design using Minitab. Analysis includes interpretation of Minitab output in voice and text!

We are sure, you will find this video extremely useful for Quality Practitioners, Reliability Engineers, Design and Process Engineers, Six Sigma Professionals, Design for Six Sigma practitioners. Some corrections at 5:21 — The R-sq value is 95.45, R-sq Adjusted is 92.53 and R-sq Predicted is 86.63%. There is some error in these figures in the video. Apologise for the oversight.

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DOE-6: Case Study in Creating Full Factorial Design in Minitab: Optimization of Fatigue Strength

DOE-6: Case Study in Creating Full Factorial Design in Minitab: Optimization of Fatigue Strength

Hello friends, We are happy to release this sixth video on Design of Experiments (DOE)! In this video, Hemant Urdhwareshe has illustrated step by step, how to create a full factorial design in Minitab software. In the next video, Hemant will illustrate complete analysis of the design in Minitab! We are sure, you will find this video extremely useful for Quality Practitioners, Design and Process Engineers, Six Sigma Professionals, Design for Six Sigma practitioners.

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DOE – What are You?

DOE – What are You?

The history of Design of Experiments (D.O.E) can be traced back to the work of various individuals, including Genichi Taguchi, a Japanese engineer and statistician. Taguchi made significant contributions to the field, particularly in the area of robust design, which aimed to improve the quality of products and processes. His work was influenced by the need for quality improvement in post-World War II Japan. Taguchi’s methodology, known as the Taguchi methods, was based on the concept of “robust parameter design,” which aimed to make processes and products insensitive to environmental factors or other variables that were difficult to control.

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DOE-5: Fractional Factorial Designs, Confounding and Resolution Codes

DOE-5: Fractional Factorial Designs, Confounding and Resolution Codes

In this video, Hemant Urdhwareshe explains basic concepts of Fractional Factorial Design, Confounding or Aliasing and Resolution of designs. View earlier videos in this series: Introduction, Application, Coded and Uncoded Values, and a Case study.

Hemant is a Fellow of American Society for Quality (ASQ) and is certified by ASQ as Six Sigma Master Black Belt (CMBB), Black Belt (CSSBB), Reliability Engineer (CRE), Quality Engineer (CQE) and Manager of Quality and Organizational Excellence (CMQ/OE).

The video would be useful to all those who wish to learn about use of fractional factorial designs to reduce number of runs in experiments. It will be also be greatly useful for preparing ASQ certification exams!

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DOE-4: Case Study in Design of Experiments to Maximize Fatigue Strength of Crankshaft

DOE-4: Case Study in Design of Experiments to Maximize Fatigue Strength of Crankshaft

Hemant Urdhwareshe, Director of Institute of Quality and Reliability presents case study to maximize fatigue strength of crankshaft using design of experiments using SigmaXL Template of Full Factorial Design with three factors each at two levels.

Hemant is Fellow of ASQ and possesses five certifications from ASQ including Six Sigma Master Black Belt and Reliability Engineer. It is recommended that viewers first watch the previous videos DOE-1, DOE-2 and DOE-3 before watching this video. SigmaXL trial version can be downloaded from www.sigmaxl.com.

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DOE-3: Coded and Uncoded Values & Establishing Regression Equation

DOE-3: Coded and Uncoded Values & Establishing Regression Equation

I am happy to share my third video on Design of Experiments (DOE-3). This is the third video in our series on Design of Experiments. It is recommended that viewers should watch the first two videos (doe-1-introduction-to-design-of-experiments and DOE-2: DOE Case Study of Spot Welding) before watching this video for better learning and understanding.

In this video, we have explained how to convert uncoded values of factor-levels to coded values and vice versa. We have also explained how to create a linear regression equation from the results of a full factorial two-level experiment.

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DOE-2: Application of Design of Experiments for Spot Welding Process

DOE-2: Application of Design of Experiments for Spot Welding Process

Dear Friends, we hope you have seen our first video on Introduction to Design of Experiments DOE)! Here is my second video on DOE! I have illustrated a case study of DOE application to Spot Welding process! The Case study is about application of Full Factorial Design with three factors, each at two levels. The video assumes that viewers have seen our first video on Introduction to DOE.

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DOE-1: Introduction to Design of Experiments

DOE-1: Introduction to Design of Experiments

Dear Friends, this video is created to provide a simple introduction to Design of Experiments (DOE). DOE is a proven statistical tool and is known to be superior to the conventional approach of One-Factor-at-a-Time of OFAT! In OFAT, one tries to optimize the settings of various factors one by one. But this has a major limitation of not getting information about interaction of the factors! DOE helps us to estimate effects of various factors along with their interactions! This is the first in the series of videos that we wish to create and share in near future. Our future videos on DOE will include practical application examples of DOE, analysis and interpretation, optimization using DOE, Fractional Factorial Designs. We will also introduce later to Response Surface Designs.

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Design of Experiments Process — A Decision Making Focus

Design of Experiments (DOE) is often only taught as a series of tools. Instead, we have used it as a tool in the Decision-Making Process which enhances implementation.

Each section below contains a 2-minute video to expand on the topic provided.

Where do we get started?

The setup and project definition are important for future success. Often, people want to jump to a solution. Then, they can start implementation and testing. Briefly pulling back to define the goal and appropriate outputs will help tremendously.

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DOE Supports World Class Quality

DOE Supports World Class Quality

This is an example of a practical DOE that was used in the development of a manual window mechanism for a truck application. A similar process can be used for current mechanical and electrical design and development projects. In this article, you will see that the DOE method:

  • is superior to the design-test-fix cycle
  • builds knowledge of which factors are important to product performance
  • quantifies the effect of a combination of factors
  • allows performance prediction

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The Basis of Design of Experiments is Comparison

The Basis of Design of Experiments is Comparison

A t-test Example

The basis of DOE analysis is comparing two samples. This methodology can be used just as well for changing one variable or more than one variable as in DOEs. Comparison of means is as simple as the t-test.

Some people initially feel intimidated considering the connection of DOE with statistics. Once they understand it in simple terms, the topic comes together for them.

So let’s see if we can accomplish that for you now.  [Read more…]

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Design Of Experiments In Industrial Testing and How It Relates To Your Business

Design Of Experiments In Industrial Testing and How It Relates To Your Business

Movement in Design of Experiments Testing

Historically, it has been relatively easy to meet customer system requirements using basic testing techniques. Today, however, customers require faster changes in technology and requirements are getting more difficult to meet. Competitive cost pressures have made the achievement of goals an even tougher task. In some cases, customers have requested and in a few cases mandated tools for companies to “be more efficient and organized in testing”. These facts create the need for organized and effective testing methods to be incorporated in all phases of the development process. [Read more…]

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Taguchi Design of Experiments Approach

Taguchi Design of Experiments Approach

Dr. Taguchi was an engineer, not a statistician. He considered the ability of design of experiments (DOE) to identify and reduce sources of variability, yet needed a system that did not require a statistician to implement.

Dr. Taguchi proposed a few considerations for those applying the Taguchi design of experiments approach. [Read more…]